{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Store Tour - Python API\n",
    " \n",
    "This notebook contains a tour/reference for the Hopsworks feature store Python API on Databricks. The notebook is meant to be run on Databricks after setting up Hopsworks to work with Databricks on AWS [(see here)](https://hopsworks.readthedocs.io/en/latest/user_guide/hopsworks/featurestore.html#connecting-from-databricks-notebooks).\n",
    "\n",
    "The notebook is designed to be used in combination with the Feature Store Tour on Hopsworks, it assumes that you have run the following feature engineering job: [job](https://github.com/logicalclocks/hops-examples/tree/master/featurestore_tour) (**the job is added automatically when you start the feature store tour in Hopsworks. You can run the job by going to the 'Jobs' tab to the left in the Hopsworks project home page**). \n",
    "\n",
    "In this notebook we will run queries over this feature store model. We will also create new feature groups and training datasets.\n",
    "\n",
    "We will go from (1) features to (2) training datasets to (3) A trained model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"></div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from hops import featurestore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connecting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Mounts successfully refreshed.\n",
       "<span class=\"ansired\">Out[</span><span class=\"ansired\">2</span><span class=\"ansired\">]: </span>True</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We need to mount an S3 bucket with dbfs the first time we run this\n",
    "dbutils.fs.mount(\"s3a://hops.databricks\", \"/mnt/certs\")\n",
    "# After the mount was created, we only need to run refresh mounts in a new cluster to make it available\n",
    "# dbutils.fs.refreshMounts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"></div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.connect('ip-172-31-7-114.us-west-2.compute.internal', 'demo_featurestore_admin000', region_name='us-west-2', cert_folder='/dbfs/mnt/certs', trust_store_path='/dbfs/mnt/certs/cacerts.pem')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get The Name of The Project's Feature Store\n",
    "\n",
    "Each project with the feature store service enabled automatically gets its own feature store created. This feature store is only accessible within the project unless you decide to share it with other projects. The name of the feature store is `<project_name>_featurestore`, and you can get the name with the API method `project_featurestore()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"><span class=\"ansired\">Out[</span><span class=\"ansired\">4</span><span class=\"ansired\">]: </span>&apos;demo_featurestore_admin000_featurestore&apos;</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.project_featurestore()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get a List of All Feature Stores Accessible in the Current Project \n",
    "\n",
    "Feature Stores can be shared across projects in a multi-tenant manner, just like any Hopsworks-dataset can. You can read more about sharing datasets at [hops.io](hops.io), but in essence to share a dataset you just have to right click on it in your project. The feature groups in the feature store are located in a dataset called `<project_name>_featurestore.db` in your project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"><span class=\"ansired\">Out[</span><span class=\"ansired\">12</span><span class=\"ansired\">]: </span>[&apos;demo_featurestore_admin000_featurestore&apos;]</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_project_featurestores()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Querying The Feature Store\n",
    "\n",
    "The feature store can be queried programmatically and with raw SQL. When you query the feature store programmatically, the library will infer how to fetch the different features using a **query planner**. \n",
    "\n",
    "When interacting with the feature store it is sufficient to be familiar with three concepts:\n",
    "\n",
    "- The **feature**, this refer to an individual versioned and documented feature in the feature store, e.g the age of a person.\n",
    "- The **feature group**, this refer to a documented and versioned group of features stored as a Hive table that is linked to a specific Spark/Numpy/Pandas job that takes in raw data and outputs the computed features.\n",
    "- The **training dataset**, this refer to a versioned and managed dataset of features, stored in HopsFS as tfrecords, .csv, .tsv, or parquet.\n",
    "\n",
    "A feature group contains a group of features and a training dataset contains a set of features, potentially from many different feature groups.\n",
    "\n",
    "When you query the feature store you will always get back the results in a pandas dataframe. This is for scalability reasons. If the dataset is small and you want to work with it in memory you can convert it into a pandas dataframe or a numpy matrix using one line of code as we will demonstrate later on in this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fetch an Individual Feature\n",
    "\n",
    "When retrieving a single feature from the featurestore, the hops-util-py library will infer in which feature group the feature belongs to by querying the metastore, but you can also explicitly specify which featuregroup and version to query. \n",
    "\n",
    "If there are multiple features of the same name in the featurestore, it is required to specify enough information to uniquely identify the feature (e.g specify feature group and version). If no featurestore is provided it will default to the project's featurestore.\n",
    "\n",
    "To read an individual feature, use the method `get_feature(feature_name)`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without specifying the feature store, feature group and version, the library will infer it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2403.3704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3390.3755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13547.4290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9678.3330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also explicitly specify the feature store, feature group, the version, and the return format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12957.0760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2403.3704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3390.3755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13547.4290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9678.3330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_feature(\n",
    "    \"team_budget\", \n",
    "    featurestore=featurestore.project_featurestore(), \n",
    "    featuregroup=\"teams_features\", \n",
    "    featuregroup_version = 1\n",
    ").head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fetch an Entire Feature Group\n",
    "\n",
    "You can get an entire featuregroup from the API. If no feature store is provided the API will default to the project's feature store, if no version is provided it will default to version 1 of the feature group. The return format is as a pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_budget</th>\n",
       "      <th>team_id</th>\n",
       "      <th>team_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2403.3704</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3390.3755</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13547.4290</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9678.3330</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_featuregroup(\"teams_features\").head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The default parameters can be overriden:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_budget</th>\n",
       "      <th>team_id</th>\n",
       "      <th>team_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12957.0760</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2403.3704</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3390.3755</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13547.4290</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9678.3330</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_featuregroup(\n",
    "    \"teams_features\", \n",
    "    featurestore=featurestore.project_featurestore(), \n",
    "    featuregroup_version = 1\n",
    ").head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fetch A Set of Features\n",
    "\n",
    "When retrieving a list of features from the featurestore, the hops-util-py library will infer which featuregroup the features belongs to by querying the metastore. If the features reside in different featuregroups, the library will also try to infer how to join the features together based on common columns. If the JOIN query cannot be inferred due to existence of multiple features with the same name or non-obvious JOIN query, the user need to supply enough information to the API call to be able to query the featurestore. If the user already knows the JOIN query it can also run featurestore.sql(joinQuery) directly (an example of this is shown further down in this notebook). If no featurestore is provided the API will default to the project's featurestore."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Example of querying the feature store for a list of features without specifying the feature groups and feature store:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_budget</th>\n",
       "      <th>average_player_age</th>\n",
       "      <th>average_attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>25.67</td>\n",
       "      <td>2701.0522</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12957.0760</td>\n",
       "      <td>25.88</td>\n",
       "      <td>92301.0860</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4134.0903</td>\n",
       "      <td>26.18</td>\n",
       "      <td>2823.9960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_features(\n",
    "    [\"team_budget\", \"average_attendance\", \"average_player_age\"]\n",
    ").head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also explicitly specify the feature groups where the features reside. Either the feature groups and versions can be specified by prepending feature names with `<feature group name>_<feature group version.`, or by providing a dict with entries of `<feature group name> -> <feature group version>`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_attendance</th>\n",
       "      <th>team_budget</th>\n",
       "      <th>average_player_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3271.9340</td>\n",
       "      <td>16758.0660</td>\n",
       "      <td>25.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2695.4463</td>\n",
       "      <td>14580.9480</td>\n",
       "      <td>25.67</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2701.0522</td>\n",
       "      <td>9290.6380</td>\n",
       "      <td>25.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>92301.0860</td>\n",
       "      <td>12957.0760</td>\n",
       "      <td>25.88</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2823.9960</td>\n",
       "      <td>4134.0903</td>\n",
       "      <td>26.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_features(\n",
    "    [\"teams_features_1.team_budget\", \n",
    "     \"attendances_features_1.average_attendance\", \n",
    "     \"players_features_1.average_player_age\"]\n",
    ").head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_budget</th>\n",
       "      <th>average_player_age</th>\n",
       "      <th>average_attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16758.0660</td>\n",
       "      <td>25.65</td>\n",
       "      <td>3271.9340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14580.9480</td>\n",
       "      <td>25.67</td>\n",
       "      <td>2695.4463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9290.6380</td>\n",
       "      <td>25.67</td>\n",
       "      <td>2701.0522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12957.0760</td>\n",
       "      <td>25.88</td>\n",
       "      <td>92301.0860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4134.0903</td>\n",
       "      <td>26.18</td>\n",
       "      <td>2823.9960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_features(\n",
    "    [\"team_budget\", \"average_attendance\", \"average_player_age\"],\n",
    "    featurestore=featurestore.project_featurestore(),\n",
    "    featuregroups_version_dict={\n",
    "        \"teams_features\": 1, \n",
    "        \"attendances_features\": 1,\n",
    "        \"players_features\": 1\n",
    "    }\n",
    ").head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you have a lot of name collisions and it is not obvious how to infer the JOIN query to get the features from the feature store. You can explicitly specify the argument `join_key` to the API (or you can provide the entire SQL query using the API method `.sql` as we will demonstrate later on in the notebook)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_budget</th>\n",
       "      <th>average_player_age</th>\n",
       "      <th>average_attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16758.0660</td>\n",
       "      <td>25.65</td>\n",
       "      <td>3271.9340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14580.9480</td>\n",
       "      <td>25.67</td>\n",
       "      <td>2695.4463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9290.6380</td>\n",
       "      <td>25.67</td>\n",
       "      <td>2701.0522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12957.0760</td>\n",
       "      <td>25.88</td>\n",
       "      <td>92301.0860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4134.0903</td>\n",
       "      <td>26.18</td>\n",
       "      <td>2823.9960</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_features(\n",
    "    [\"team_budget\", \"average_attendance\", \"average_player_age\"],\n",
    "    featurestore=featurestore.project_featurestore(),\n",
    "    featuregroups_version_dict={\n",
    "        \"teams_features\": 1, \n",
    "        \"attendances_features\": 1,\n",
    "        \"players_features\": 1\n",
    "    },\n",
    "    join_key = \"team_id\"\n",
    ").head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Advanced Eamples of Fetching Sets of Features and Common Pitfalls"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting 12 features from 4 different feature groups:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_position</th>\n",
       "      <th>average_player_age</th>\n",
       "      <th>average_attendance</th>\n",
       "      <th>team_budget</th>\n",
       "      <th>sum_player_worth</th>\n",
       "      <th>sum_position</th>\n",
       "      <th>sum_attendance</th>\n",
       "      <th>team_position</th>\n",
       "      <th>average_player_rating</th>\n",
       "      <th>average_player_worth</th>\n",
       "      <th>sum_player_age</th>\n",
       "      <th>sum_player_rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>55.15</td>\n",
       "      <td>25.65</td>\n",
       "      <td>3271.9340</td>\n",
       "      <td>16758.0660</td>\n",
       "      <td>30787.268</td>\n",
       "      <td>1103.0</td>\n",
       "      <td>65438.680</td>\n",
       "      <td>26</td>\n",
       "      <td>322.69797</td>\n",
       "      <td>307.87268</td>\n",
       "      <td>2565.0</td>\n",
       "      <td>32269.797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>62.45</td>\n",
       "      <td>25.67</td>\n",
       "      <td>2695.4463</td>\n",
       "      <td>14580.9480</td>\n",
       "      <td>22179.488</td>\n",
       "      <td>1249.0</td>\n",
       "      <td>53908.926</td>\n",
       "      <td>36</td>\n",
       "      <td>229.57967</td>\n",
       "      <td>221.79488</td>\n",
       "      <td>2567.0</td>\n",
       "      <td>22957.967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>58.45</td>\n",
       "      <td>25.67</td>\n",
       "      <td>2701.0522</td>\n",
       "      <td>9290.6380</td>\n",
       "      <td>18485.990</td>\n",
       "      <td>1169.0</td>\n",
       "      <td>54021.043</td>\n",
       "      <td>37</td>\n",
       "      <td>227.61397</td>\n",
       "      <td>184.85991</td>\n",
       "      <td>2567.0</td>\n",
       "      <td>22761.396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>29.05</td>\n",
       "      <td>25.88</td>\n",
       "      <td>92301.0860</td>\n",
       "      <td>12957.0760</td>\n",
       "      <td>792043.250</td>\n",
       "      <td>581.0</td>\n",
       "      <td>1846021.800</td>\n",
       "      <td>1</td>\n",
       "      <td>7191.86330</td>\n",
       "      <td>7920.43260</td>\n",
       "      <td>2588.0</td>\n",
       "      <td>719186.300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74.15</td>\n",
       "      <td>26.18</td>\n",
       "      <td>2823.9960</td>\n",
       "      <td>4134.0903</td>\n",
       "      <td>17936.293</td>\n",
       "      <td>1483.0</td>\n",
       "      <td>56479.920</td>\n",
       "      <td>43</td>\n",
       "      <td>181.49428</td>\n",
       "      <td>179.36293</td>\n",
       "      <td>2618.0</td>\n",
       "      <td>18149.428</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_features(\n",
    "    [\"team_budget\", \"average_attendance\", \"average_player_age\",\n",
    "    \"team_position\", \"sum_attendance\", \n",
    "     \"average_player_rating\", \"average_player_worth\", \"sum_player_age\",\n",
    "     \"sum_player_rating\", \"sum_player_worth\", \"sum_position\", \n",
    "     \"average_position\"\n",
    "    ]\n",
    ").head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a training dataset from the Feature Store\n",
    "\n",
    "The feature store has an abstraction of a **training dataset**, which is a dataset with a set of features (potentially from many different feature groups) and labels (in case of supervised learning).\n",
    "\n",
    "When you train a machine learning model, you want to use all features that have predictive power and that the model can learn from. At this point, we can create a training dataset of features from several different feature groups and use that for training. That is the purpose of the training dataset abstraction.\n",
    "\n",
    "Of course you can always just save a group of features anywhere inside your project, e.g as a csv, or .tfrecords file. However, by using the feature store you can create managed training datasets. Managed training datasets will show up in the feature registry UI and will automatically be versioned, documented and reproducible.\n",
    "\n",
    "Lets create a dataset called *team_position_prediction* by using the previous set of 12 relevant features from the featurestore. We will combine features from four different feature groups to form this training dataset: \n",
    "\n",
    "- teams_features\n",
    "- attendances_features\n",
    "- players_features\n",
    "- season_scores_features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"></div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "feature_list = [\"team_budget\", \"average_attendance\", \"average_player_age\",\n",
    "    \"team_position\", \"sum_attendance\", \n",
    "     \"average_player_rating\", \"average_player_worth\", \"sum_player_age\",\n",
    "     \"sum_player_rating\", \"sum_player_worth\", \"sum_position\", \n",
    "     \"average_position\"\n",
    "    ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can create a training dataset with the list of features with some extended metadata such as schema (automatically inferred). By default when you create a training dataset it will be in \"tfrecords\" format and statistics will be computed for all features. After the dataset have been created you can view and/or update the metadata about the training dataset from the Hopsworks featurestore UI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First you should check if a training dataset with the same name has been created before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">1\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "latest_version = featurestore.get_latest_training_dataset_version(\"team_position_prediction\")\n",
    "print(latest_version)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can use the `featurestore.create_training_dataset()` API to create and launch a job which will create your training dataset. The job will be called the same name as your training dataset, in case you want to rerun the creation from the Hopsworks UI. Good practice is to increase the version by 1, but you can also decide to overwrite it with the same version if you set the `overwrite` argument to `True`.\n",
    "\n",
    "By default the dataset is written as \"tfrecords\" to HopsFS but you can specify an alternative `sink` by passing your storage connector name. Please note that the storage connector has to be created in the Hopsworks featurestore UI previously.\n",
    "\n",
    "The `featurestore.create_training_dataset()` API offers additional parameters to modify its behaviour, for the full range of possible arguments please refer to the docs. Most arguments will be familiar to you from the `featurestore.get_features()` API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\">Training Dataset job successfully started\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.create_training_dataset(\n",
    "    features = feature_list, training_dataset = \"team_position_prediction\",\n",
    "    descriptive_statistics = False,\n",
    "    feature_correlation = False,\n",
    "    feature_histograms = False,\n",
    "    cluster_analysis = False,\n",
    "    training_dataset_version = latest_version + team_budget\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Free Text SQL Query from the Feature Store\n",
    "\n",
    "For complex queries that cannot be inferred by the helper functions, enter the sql directly to the method `featurestore.sql()` it will default to the project specific feature store but you can also specify it explicitly. If you are proficient in SQL, this is the most efficient and preferred way to query the feature store."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Without specifying the feature store the query will by default be run against the project's feature store:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_budget</th>\n",
       "      <th>team_id</th>\n",
       "      <th>team_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12957.0760</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2403.3704</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3390.3755</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13547.4290</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.sql(\"SELECT * FROM teams_features_1 WHERE team_position < 5\").head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also specify the featurestore to query explicitly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>team_budget</th>\n",
       "      <th>team_id</th>\n",
       "      <th>team_position</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12957.0760</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2403.3704</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3390.3755</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13547.4290</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.sql(\"SELECT * FROM teams_features_1 WHERE team_position < 5\",\n",
    "                featurestore=featurestore.project_featurestore()).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Featuregroup Visualization\n",
    "\n",
    "As you will see later on in this tutorial, when writing to the featurestore there is an option to also compute statistics of the features. The computed statistics will be stored as attached metadata of featuregroups and visualized in the feature registry. You can also access the statistics from the python API and visualize it in Jupyter notebooks in `%%local`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(featurestore.visualize_featuregroup_distributions(\"players_features\", plot=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also override default parameters and configure the plotting options:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(featurestore.visualize_featuregroup_distributions(\"players_features\", \n",
    "                                                  featurestore=featurestore.project_featurestore(), \n",
    "                                                  featuregroup_version=1, \n",
    "                                                  figsize=(12, 9),\n",
    "                                                  color='lightblue', \n",
    "                                                  log=False, \n",
    "                                                  align=\"center\", \n",
    "                                                  plot=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lmiRfL6W8NcnwJF9M8t2qquYmSSll9yQ/S3JGVVV/bBQ971M3pJEJ8mBVVX/erBcAbFEEQAAAAACgCbaxJbA2xulJvpTk+iTdSS5NPZtjtc4k05Jst45j+OkCAiAAAAAAwJajqqpFSV6/ju1/TtKxnmOsczuwbWhv9gAAAAAAAAA2NQEQAAAAAACg5VgCCwAAAACaoFuVCoAhJQMEAAAAAABoOQIgAAAAAABAyxEAAQAAAAAAWo4aIAAAAADQBDU1QACGlAwQAAAAAACg5bTVhJphXfwPAgAAAEOvrdkDaIavX791P3d403Hb5u8N2HpYAgvW48/3Vc0eAluRSVNLruoszR4GW5mTuyrzho12clflHsVGcY9iMNyjGIyTu6p8+Fsrmj0MtiIfPWt4s4cAQIsSAAEAAACAJujubvYIAFqbGiAAAAAAAEDLEQABAAAAAABajiWwAAAAAKAJalt1CXSALZ8MEAAAAAAAoOUIgAAAAAAAAC1HAAQAAAAAAGg5aoAAAAAAQBOoAQIwtGSAAAAAAAAALUcABAAAAAAAaDkCIAAAAAAAQMtRAwQAAAAAmqBbDRCAISUDBAAAAAAAaDkCIAAAAAAAQMsRAAEAAAAAAFqOGiAAAAAA0AS12tZeBKSt2QMAWCcZIAAAAAAAQMsRAAEAAAAAAFqOJbAAAAAAoAm2+hWwALZwMkAAAAAAAICWIwACAAAAAAC0HAEQAAAAAACg5agBAgAAAABN0N3d7BEAtDYZIAAAAAAAQMsRAAEAAAAAAFqOAAgAAAAAANBy1AABAAAAgCao1Zo9AoDWJgMEAAAAAABoOQIgAAAAAABAyxEAAQAAAAAAWo4aIAAAAADQBN1qgAAMKRkgAAAAAABAyxEAAQAAAAAAWo4lsAAAAACgCWqWwAIYUjJAAAAAAACAliMAAgAAAAAAtBwBEAAAAAAAoOWoAQIAAAAATVDr3tqLgLQ1ewAA6yQDBAAAAAAAaDkCIAAAAAAAQMsRAAEAAAAAAFqOGiAAAAAA0ARbfQkQgC2cDBAAAAAAAKDlCIAAAAAAAAAtRwAEAAAAAABoOWqAAAAAAEAT1NQAARhSMkAAAAAAAICWIwACAAAAAAC0HEtgAQAAAEATdHdbAwtgKMkAAQAAAAAAWo4ACAAAAAAA0HIEQAAAAAAAgJajBggAAAAANEFNCRCAISUDBAAAAAAAaDkCIAAAAAAAQMsRAAEAAAAAAFqOGiAAAAAA0ARqgAAMLRkgAAAAAABAyxEAAQAAAAAAWo4lsGAb1dXVlW9d8l/52c9/niVLlmTvyZPzhjNen+cccvB6912wYEH+/WsX5eZbbk13rZaDDnx23vKmszNxwoSePitWrMgXv/yVVPfOyPxHH013d3cmTpiQE44/Lq84+aR0dHQM5eUxBDq2G5Up7zknOz/3wOz83Genc8xOue3s8/PQf16xQfsP23GH7H/Be7PbK45Lx3Yjs+gPd+Se9/5rHr/1nn59x59yTKZ96O3ZYf+pWT5vQWZ/67LM+MSXk+7uTX1ZDDHzhsFwj2Jjea1hMMwbnq4RnckJh3Vkvz3b0zkseejRWq7+46rMXbj+NY1edWRHDt6n/2dSH/1r8qUruvq0jRmdHP+cYdl7YluGtSdzFtbys1tW5YFHrJ0EAOsjA2QrVEr5SCnllmaPY0tWSjmrlPJYs8exJfvM57+Qy6/4UY57yUvytjefm46Ojnzwnz+au+7u/4att6XLluU9538gd951d07/X6/LWa8/PfffPzP/eP4Hsnjxkp5+y5evyIMPzs4Rzz0sZ7/hzJx79v/JPlP2zle//o185vNfGOrLYwgMHzcm+37gbdmhTMnjt03f6MVqn/vjr2fi607OA1+6JPec/5kM33VMnnf9Jdluyp59+u16wgtz2KVfyoqFf81d7/pYHrni+kx9/1vzzC98cFNeDpuJecNguEexsbzWMBjmDU/XGccNy7Mmt+d301fl2j+tyvYjkzeeMCxjRm/Y/itXJT/41ao+X9f+cWWfPjtul5x7Umf2HN+WX9+5KtfdvCqdw5IzXzose41vG4KrYnPrrtW26i+ALZ0MkK2Xu0xDKWVWkgurqvq3NTb5Ga3F9Ore/OJXv865Z78xrz71lUmSY495Sc5929/loou/mQs/c8Fa9/3xlVdlzty5+eKFn8u+U/dJkhx26HNy7tvekUsv/2H+z5mvT5KMHr1DvvC5T/fZ9+QTX5btttsuP77qJ3nzm87OmJ13HqIrZCgse3hert/jyKyYvzA7PueZOeqmSzd434mvOTFjnndw/vS6d+SRK65Pksy59Oq8+O6rM+0j78ytZ/1jT9/9L3hvHr/tnvz+pLN7HkSsXPxE9nnfuXngi9/OEzMe2KTXxdAyb9hY7lEMhtcaBsO84el45qT27LFrW/7fz1fmnr/Uf693PdCdd57amWMO6sgPfr1qvcfo7k7umLXuLKCjn92REZ31rJCFi+ttf5rRnXe+qjMnPrcjX71q5Tr3B4BtnQwQBq2U0tT1IUopnc08/9bsV7/5TTo6OnLSy47vaRve2ZmXvfSluWd6lUcfXbCOfW/KtH2n9jxYSpI999gjhxx0YH75q1+v99y7jd81SfLEE088jSugGWorV2bF/IWD2nfCqcdn+dxHex4QJEnXgscy59Krs9vLj03bsHo8fof9pmSH/ffJXy76Xp9PYf75K99JW3t7Jpx2wtO7CDY784aN5R7FYHitYTDMG56OAya1ZcnS9AQ/kuTJ5fUgyH57tad9A5Mz2pIMX8dHUyeNb8uchbWe4EdSzxyZ/mB3Jo5t2+BsEwDYVskASVJKeU2SDyeZmuTJJDcneVWSq5LcUlXVu3v1vTzJY1VVvbHx71lJLkoyLclpSRYkeUeSmxrtxyaZmeSNVVX9aQPGclaSLyR5Q5LPJNkzyS+SnFNV1ey17HNYkk8mOSRJZ5Jbk5xXVdUtje3fSDK+qqqX99pnWJKHkpxfVdXFpZS2JOcneVOSCUmqJB+vquoHjf4vSnJjkpOSfDzJs5Icn+SXaxnTjkkWJjm8qqqbG8dfkGR6VVUvaPR5fZJPVlW1V+Pfz0ryf5M8P/Xfww+SvLuqqica2y9OsnOSPyR5e5JlpZS/JJmU5MJSyheS1Kqq6ug1juMbP889k/w6yRuqqnpkrb+AbcT9M2flGc/YPaNGjerTXsq+je0zM27cLv32q9VqmfXAA3nZ8cf121amTcvNt96WpcuWZdTIkT3tK1euzJNPPpnlK1akundGLr38iuw2fnx2nzhxE18VW7IdD94/f73lrn7ti/5we/Y6+7XZftrkLLn7vux48AFJrZa/3ty37/K587Ns9tzsdPABm2vIbAHMm22TexSbm9caBsO8YeLY9sxZ0H/RgdmP1nLovskuO7Vl/qJ1L0rQOSx5/+md6RyWLFue3PFAd67906p09Urq6Ohoy9Ll/Y+zus/uY9vz2GK1ZLZmNb8+gCG1zWeAlFImJPlO6sGK/ZK8KMllqX8QY0P9fZJfJTk4yZVJLknyrcb3Q5Lc3/j3htouyfuTvD7JC1J/6P/ddfQfneSbjb5HJLk3yU9KKds3tl+U5IRSym699nl5klFJ/rvx79XnOzfJAUkuTHJJKeXoNc71qSTvS7J/ktvXNqCqqh5PckuSFzeanp2kO8khpZTtGm0vTPLzJGm0XZN6kOTQJK9JclySL65x6GNTDzYdl+SUJKcmmZ3kQ6kHbno/sdg+yT8k+d9Jjk6yV5LPrm3M25KFCx/L2DFj+rXvMmZsarVaFiwc+JNwixcvTldXV8aOGdtv29ix9eMtXNB331//9qa89vQz8vo3nJ2PffJfs+u4cfmXD38w7e3b/MvPNmXkxPFZPnd+v/blc+b3bE+SERPqn75eNmdev77L5s7PiN3HD+Eo2dKYN9sm9yg2N681DIZ5w+jtksVL+wcmljTadhzVb1Mfi5/8/+zdd5ycVb348c9sTQ8hkAIkARI4IaCUUJQiGBBE8F5Ahd9FISjYBSxXwX5FUbFhu4pXuIj1CgiiooChREVEmpSQnARIQgIJpJK+2d2Z3x/nmWS2ZLO7yWZ2J5/367Wv3X3qeWbOPOeZ8z0F7n8qz633N3HT9GZmLchzRKjivJNqWlRGLH2lwMhhOWpbNV8dNzJtNWQgkiSpA/YASRXm1cCtMcYF2bIZACGEzh7j9hjjtdk+XwQ+APyzpPfEVcDfQwgjYoxtn3zbqgE+GGN8ONt/KjAzhHB4cVmpGOO9pf+HEN4HnEMK5vwxxvhACGE2cB6bAwAXADfFGNeHEOqATwInxhgfzNbPy4If7yUFd4o+G2O8uxPXAKnnygnAt7Lfd5GCTMdmf58AFAfyfjtQD5wfY9yQXe+HgN+HEC6LMRa/Xawh9YbZ1CYmhNAMrGnnta0B3htjnJdt931SoGSnt3FjA3W1bUcQq61Lyxo2bmx3v+Ly2nb2raury7ZpaLH8kINfzVevvIK1a9by2ONP8Nzcuaxfv36b0q++p6p/PfmGtvkqv6EBcjmq+qcW2dXZ73xDY7vb1gz2G97OxHyzc7KM0o7mvUbdYb5RTTU0t9Nyv6kZyEHNVmpb7n6sdI6QAjPmw7JVBU48tJpJ46qYMT8d/KHYTBhTwznH1zDt0WY2NhU4amI1ewxPAZDasg5MLUlS72cABB4H7gaeCiHcSaqYvznGuLILx3iy+EeM8aUscPJUyfqXSD1KRgCdCYA0lQY6YowxhLCS1OuiTQAkhDACuJIU8BhBCuj0J/V4KLqWNLzVNz9zCagAACAASURBVLKeIKeyuXfGBFKvkz9nQ1UV1ZKGAysqAFsdxqvEdOBd2TGPJ/XwWAycEEJ4MjtvMXgzEXg8C34U3U/qpRSAYgDkydLgx1asKwY/MotIr89Or66uno2Nbb+ENW5My+qziqLWissb29l3Y1bxVF9X32L5LkOHcujBBwNw7DFH86sbb+KTn/kc11/7IyeY3Ynk1zdQVd82X1X1q4dCgfz69NFvzn5X1betwKzqV0/z+oY2y1W5zDc7J8so7Wjea9Qd5pudR1UO+rcsPli3IQU6qtvpMFhTDRSgqRtzkz/wdJ4ph1Yzfo8cM+anZc+8WOD2B5t5w2HVvO/0GsjB8lUw7dFmTj68mnZia5IkqcRO378/xpiPMZ4MvJHU8+NiYFYIYW/SkE2th8Jqb+Lt9h45SpcV+8X21Ov9U+DVpLS/FjiYNP9GXatt9g0hHEUa6uq5GOPfs3WDst9vyvYt/kwC3tbqXF2ZFfQvpOG5JrN5uKvpwOtJAZEXYozPdeF4XT1/6/elQNeGNqtYu+46jOUrVrRZvmxFGhpk+K5thw8BGDx4MLW1tSxf0Xb4keXL0/F2Hd7+vkWvO+YY1m/YwAP/eLDD7VRZNix6edMQEKXqR7ccFqI4lERx2IhS/UbtTsOLnYkhq1KYb3ZOllHa0bzXqDvMNzuPsSNyfPzsWj7+ttpNv4cMTENYDe7f9uvloGzZqm50KGzKw/oG6F/X8rgPxTxfu7GRa//UxDV/aOK7v21MgY9C6jWivq1QKPTpH0nq7Xb6AEhRjPGBGOMXSHN2NJImQV9CyZwSIYQq0uTf3dGVUqEmm9i8eN5Amgfk6S1sfzTw3RjjnTHGmaT071a6QYxxOfBb4F3AVOD6ktVPAw3AuBjjc61+XuhCuluIMb5C6h3zIWBjjHE2KShyKGn+juklm88EDg4hlI6UeizQTJqQvSMbSb1e1Enj992HF154sc0wH7NmRXK5HOP33bfd/XK5HPvsPY7Zc55ps27W7NmMGjWyxeSy7SkOP7J27bpupl590arHZzH00APbLB921ME0r9vA2tnzsu1mQi7H0Mktb7X1o3an316jeOVfW7oNqhKZb3ZOllHa0bzXqDvMNzuPRcsL3HBXEzf8uWnT7zXrYfGKPKOHtw2AjNk9R2MzLHul6xXDdTUwoB7WtjPpeVNzmmB98fK0bvwe6TzPv2wFtCRJHdnpAyAhhCNDCJ8MIUwOIYwB3kIKHswE7gFOCyG8KQtC/JAUiOiOrvQ8aAK+l6VtMilY8fcY45aGn5oDnBdCmJj18Pg50N439+tIwY+JlEzKHmNcQ5ob5OoQwvkhhH1DCIeGED4UQjivm9dQdB9pfo/p2blWkF7bc2gZAPkFsAG4IYRwYAjh9cB3gZ+WzP+xJfOA14UQ9gghDO9GGnc6xx1zDM3Nzdx+x52bljU2NnLXtHuYGPZnt93Sy/jykiUsWLiw1b5HM3vOM8x55tlNyxYsXMi/Hn+C4489dtOyVatWtXvuP91xF7lcjv33m7A9L0m9SP3I3Ri4/z5QMonw4lvupH7kcEad8YZNy2qHD2PUWafw0h/uoZCNEbBm5rOsmfUcYy86u8Uxx73vXAr5PItvvWvHXIR2OPONiiyj1JO816g7zDc7t4ZGmLu40OKnOQ8z5hUY1A8OGLv5a/KAepg0ropZC/LkS+ISwwaln6LqqhTsaO2Eg1O7vjkvtDO5SIkxu+c4YGwVj87Js7EbQ21JkrQzcQ4QWEUanulSYAgwH/hojPHOEEINaWipG0hBiatJQZFS7TW36OyyLVlLmhz8l8AepF4TF3Ww/buA/yHNz7EA+BSbJzvfJMY4LYSwiDSPxuJW6z4bQngZuBzYF1hJmv/jy928hqLppNe2dKL2+0iv630l518fQjgF+A7wT1IA52bgY504x+eAa4BnScN+2RtkKyaG/Xndscdw/U9+ysoVK9ljj9HcNe1uXn75Zf7zI5ds2u5r37yaJ5+awZ1/uG3Tsjef9ib+eOddfObzX+CtZ51JdXUVt/z2d+y66zDecua/b9ru7nvv4w9/vIOjX3sUo0eNYt369Tzy6GM89q/Hec1RR3Lwq1+1Q69Z28e4959L7dAh9NtzJAAjT59C/71SR7m53/8ZzWvWEr78MfZ6xxncM2EKGxYsAmDRb+5gn0vO59XXfoVBk/Zj47IVjHvff5CrqmL2Fd9rcY5Zl3+Nybf8gKPuuJ4Xb7ydwQcFxr3/XBZcdxNrZ8/dsRes7cJ8o66wjFJ3ea9Rd5hv1F1Pz8+zcGkVZx5Tw4hdmlnXAEeGKnI5uPdfzS22veCUWgoF+PYtaZTmQf3h/W+u5cm5eZZmPUX227OKCXvmmLOwQFyw+av30IFw9vE1xAV5Vq+HkbvkOHz/KhYvLzDtsZbnkSRJbeUcr693CSFMBa6OMXY8SHX3jj0QeAGYGmO8bWvbC4DC/Ge2NgJX39TY2MgNP/sFd993H2vWrGGfvffmgvPewWGHHrJpm49f/mmenDGDO37/2xb7Llu2jGt+fB2PPPoY+UKBg1/9Kt530YWMHj1q0zaz5zzDTbfcyqwYWblyJdVV1ey1156cOOX1/Pvpp1FVVZkd0MZNCNxeG8qdjB7z+tl303/s6HbX3bPfiWxYsIhXX/tl9nz7v3Pv/idtqiQAqBkyiAOu+gQj/+0kqvv3Y+VDTzDzsqtY9a+ZbY414vQp7P/ZDzJo4ngalixn4Q23MOfKH0C+49ZwfdVpjdF8Y77pstMaI5ZRllFdYRnlvaY7LKPMN91xWmPkczdsLHcyelx9LZxyeDUTx1RRWwMvLC1wx8PNm4apKvrIWbUU2BwAqa+FNx1ZzZjdqxg8AHI5WL6qwBPP5bn/6Tyl1TT9auGMY2rYa/cc/etg1Tp4al6evzzZTGMF9f64Ymod7KRzdn7+p419umLuC+fX7pTvm6S+wwBIL9MTAZAQQg7YndSb4mxgfIyxMp+0t7+KDYCoZ1R65ZJ6RqVXLqlnVHIARD3DMkrdYRml7thZAiDafgyA9F0GQCT1dg6BtYOFEP4IHNfOqgJpuKlF7azbVmOBuaThsaZur+BHCOEpYFw7qwrAe2OMv9oe55EkSZIkSZIkqasMgOx4FwL9t7BueYxxJSUTlG8PMcb59MyE96cCtVtY91IPnE+SJEmSJEmSpE4xALKDxRh7oodHWcQYF5Q7DZIkSZIkSX2VQ9NLUs+qzBkeJUmSJEmSJEnSTs0AiCRJkiRJkiRJqjgOgSVJkiRJkiSVQd4RsCSpR9kDRJIkSZIkSZIkVRwDIJIkSZIkSZIkqeIYAJEkSZIkSZIkSRXHOUAkSZIkSZKkMig4CYgk9Sh7gEiSJEmSJEmSpIpjAESSJEmSJEmSJFUcAyCSJEmSJEmSJKniOAeIJEmSJEmSVAYFpwCRpB5lDxBJkiRJkiRJklRxDIBIkiRJkiRJkqSKYwBEkiRJkiRJkiRVHOcAkSRJkiRJksogn3cSEEnqSfYAkSRJkiRJkiRJFccAiCRJkiRJkiRJqjgOgSVJkiRJkiSVQaHgEFiS1JPsASJJkiRJkiRJkiqOARBJkiRJkiRJklRxDIBIkiRJkiRJkqSK4xwgkiRJkiRJUhkU8uVOgSRVNnuASJIkSZIkSZKkimMARJIkSZIkSZIkVRwDIJIkSZIkSZIkqeI4B4gkSZIkSZJUBvlCodxJkKSKZg8QSZIkSZIkSZJUcQyASJIkSZIkSZKkimMARJIkSZIkSZIkVRznAJEkSZIkSZLKoOAcIJLUo+wBIkmSJEmSJEmSKo4BEEmSJEmSJEmSVHEcAkuSJEmSJEkqg3zeIbAkqSfZA0SSJEmSJEmSJFUcAyCSJEmSJEmSJKniGACRJEmSJEmSJEkVxzlAJEmSJEmSpDIoOAWIJPUoe4BIkiRJkiRJkqSKYwBEkiRJkiRJkiRVHAMgkiRJkiRJkiSp4jgHiCRJkiRJklQGhbyTgEhST7IHiCRJkiRJkiRJqjgGQCRJkiRJkiRJUsUxACJJkiRJkiRJkiqOc4BIkiRJkiRJZZAvOAeIJPUke4BIkiRJkiRJkqSKYwBEkiRJkiRJkiRVHIfAkiRJkiRJksqgkHcILEnqSfYAkSRJkiRJkiRJFccAiCRJkiRJkiRJqjgGQCRJkiRJkiRJUsVxDhBJkiRJkiSpDJwDRJJ6lj1AJEmSJEmSJElSxTEAIkmSJEmSJEmSKo4BEEmSJEmSJEmSVHGcA0SSJEmSJEkqA6cAkaSeZQ8QSZIkSZIkSZJUcQyASJIkSZIkSZKkimMARJIkSZIkSZIkVZxcoeBgg1IH/IBIkiRJktTzcuVOQDm876oVfbre4ZrLhu2U75ukvsNJ0KWtePnph8udBPUhIyYdzqf/t6HcyVAfc+W76vncDRvLnQz1MVdMrbOMUpdYRqk7LKPUHVdMreP22lDuZKgPOa0xljsJkqQK5RBYkiRJkiRJkiSp4tgDRJIkSZIkSSoDh6aXpJ5lDxBJkiRJkiRJklRxDIBIkiRJkiRJkqSKYwBEkiRJkiRJkiRVHOcAkSRJkiRJksogn3cOEEnqSfYAkSRJkiRJkiRJFccAiCRJkiRJkiRJqjgGQCRJkiRJkiRJUsVxDhBJkiRJkiSpDAoF5wCRpJ5kDxBJkiRJkiRJklRxDIBIkiRJkiRJkqSKYwBEkiRJkiRJkiRVHOcAkSRJkiRJksqgkHcOEEnqSfYAkSRJkiRJkiRJFccAiCRJkiRJkiRJqjgOgSVJkiRJkiSVgUNgSVLPsgeIJEmSJEmSJEmqOAZAJEmSJEmSJElSxTEAIkmSJEmSJEmSKo5zgEiSJEmSJEllkC84B4gk9SR7gEiSJEmSJEmSpIpjAESSJEmSJEmSJFUcAyCSJEmSJEmSJKniOAeIJEmSJEmSVAaFvHOASFJPsgeIJEmSJEmSJEmqOAZAJEmSJEmSJElSxTEAIkmSJEmSJEmSKo5zgEiSJEmSJEllUCg4B4gk9SR7gEiSJEmSJEmSpIpjAESSJEmSJEmSJFUch8CSJEmSJEmSyiCfdwgsSepJ9gCRJEmSJEmSJEkVxwCIJEmSJEmSJEmqOA6BJUmSJEmSJKnXCCEMA74PnA7kgd8Al8YY13awz0jgG8BJwGAgAlfGGG/p+RRL6q0MgEiSJEmSJEllUHAOkC35JTASOBGoA34C/Ah4Rwf7/AwYQgqaLAPeDtwYQpgcY3y8R1MrqddyCCxJkiRJkiRJvUIIYSJwCnBhjPHhGOPfgYuB/xdCGNXBrq8FvhdjfCTGOC/GeCWwEpjc86mW1FvZA0SSJEmSJElSb/FaYEWM8bGSZdOAAnAUcNsW9rsfOCeE8EdS4OMcoB64r+eSKqm3sweIJEmSJEmSpN5iFPBy6YIYYzOwPFu3JeeQhstaBjQAPwTOjDE+10PplNQH2ANEkiRJkiRJKoNCYeeZAySE8BXgsg42KQAHbMMpvgQMBaaQgiBnADeFEI6NMc7YhuNK6sMMgEiSJEmSJEnqad8Art/KNs8Bi4ERpQtDCNXArtm6NkII+wIfBCbFGGdli58MIbwuW/6BbUi3pD7MAIgkSZIkSZKkHhVjXEbqmdGhEMIDwC4hhENL5gE5EcgBD25htwGkHiStu9Q04xQA0k7NAIgkSZIkSZKkXiHGOCuEcCfw4xDC+0nzenwP+FWMcTFACGEP4G7gvBjjw8As4FngRyGEj5MCLWcCJwGnleEyJPUSBkAkSZIkSZKkMijk8+VOQm91LvB9YBqQB24GLi1ZXwvsT+r5QYyxKYRwKvBV4HfAIOAZ4PwY4507MN2SehkDIJIkSZIkSZJ6jRjjSuAdHayfD1S3WvYs8LYeTpqkPsYx8CRJkiRJkiRJUsWxB4gkSZIkSZJUBvl86zm7JUnbkz1AJEmSJEmSJElSxTEAIkmSJEmSJEmSKo4BEEmSJEmSJEmSVHGcA0SSJEmSJEkqg0LBOUAkqSfZA0SSJEmSJEmSJFUcAyCSJEmSJEmSJKniGACRJEmSJEmSJEkVxzlAJEmSJEmSpDIo5J0DRJJ6kj1AJEmSJEmSJElSxTEAIkmSJEmSJEmSKo4BEEmSJEmSJEmSVHGcA0SSJEmSJEkqA+cAkaSeZQBE2kk1NjZx7a9u4q7p97N6zVrG7z2Gd597NocffFCH+z3/wiJ+e+c0Zs55ltnPzaOxsYmbfvRtRu6+W7vb/+2fj3D9r29h3sIXGDZ0CG+acjxT33Ym1dV2QOvL6mvh1CNrOGBsFbU1sHBJgT/9s4lFyzv/8H7kxCqOCNXsNjRHYxMsXl7g9gebeGlFy2Mc96pqjpxYzeD+sHRVgemPN/Pk3Pz2viTtAPW1cMrh1Uwck/LNC0sL3PFwM4s7kW/OOKaaQ8a3vW8sfQW+f1tji2XDBsPJh9Wwz+gcNVWwaHmBux9rZt5LfrnsKyyjtC0so9QdllHqrOoB/dn3Py9ilyNezS5HvIraYUN5/MLLeeHnt3Vq/5ohgzjgqk8w8t9OonpAP1Y+9CQzP/FVVv1rZpttR5w+hf0/+0EGHTCBhpeXsfCGW5hz5Q8g731GkqTO8tudKkII4foQwi1b2ebeEMK3dlSaersrv3sNN/3+Dk45/lguveh8qquq+fiXvsaTs2Z3uN+MOIdb/vhn1m9oYO+99iSX2/K2/3jkX3z6qqsZMngQH3n3BbzuqCP46U2/5TvX3rCdr0Y72tSTa3nVPlU88HQzdzzUxMB+cNGbatl1cOf2f8txNZx2VA0vLC3whweauOexJlauKTCwX8vtTp5czcmHVzNnYZ7fP5C2OfuEGg7ax+KrLzrvpBoO2ruKB2c1c9cjzQzsB+86pYZhncw3Tc3wm782t/i56+GmFtsMGQDveVMtY0bk+NtTzfz50WZqa+D8N9QwdkQHNyz1KpZR2haWUeoOyyh1Vt1uw9jv0x9gUNiXVY/PgkLXgldH/P7HjD77NOZ9/2fMvPzr1O0+jNdM+xkD9h3TYrvdT3kdh9/8fTYuf4UZl36Rl26bxoRPvZ8Dv/2Z7Xk5kiRVPHuAqEMhhHuBx2KMHy13WrbiEsBvDZ309Oxnuef+f/DBC97OOf92KgCnnHAsUy+9jB/e8Ct+8JXPb3HfY4+azAlHH0n/fv34v9tu55l587e47X/f8Esm7D2Ob37uMqqqUmXAgP79+PlvfsdbT38jY/ccvX0vTDvEQftUMWZEjl/d08TT81Prs6fm5vnIW+s48dAabvpL01b3P2RCFb+Y1sSsBaWt11q2ZBvcH445qJp/PN3M7Q82A/DInDwXnVrLG4+o4am5G7frdalnHTiuir12z/Hr+5qY+XyqKJgxL88lZ9Yy5eBqfvO35q0eI59nqy2rj3tVNfW1qcXt8tVp2SNz8lxyRi2nHlHNj27vOH+q/CyjtC0so9QdllHqig0vvsy0vY5h45LlDDnsQI594OZO7zv6racy7DWH8MjZF/PSbdMAWHTzHZzw9B3s//lL+NfUj2/a9oCrPsGqx2fyzzdduCnI0rR6LeMvew/zvvdT1s6Zt12vS+WTL9ijR5J6ks2TVBFijKtjjKvKnY6+4r4HHqS6uoo3v+H1m5bV1dZy2oknMGP2HJYsW77FfQcPHEj/fv22uL5o3oIXmL/wRf7t5CmbKpYAznjjSeQLBe574J/bdhEqmwPHVbFmPZsqlgDWNaQKpgPGVVG1lVDkMQdWs3BJYVPFUm11+9tNyo714KyWXwgenNXM0AHYUrKPmTQux5r1bKpYgpRvZszLM3Hs1vNNUQ6o66D5xrgRORYtL2yqWILUKnfWgjyjd811uiWvyscyStvCMkrdYRmlrig0NbFxyZbLoo6MOvNkGhYv3RT8AGhctoJFN9/ByDefSK4mZaBBE/dl0AHjef7aG1v0MJl/zS/JVVUx6qxTtu0iJEnaidgDRFsUQrgeOB54XQjhw0AB2AcYAnwNOA5YC9wFfCTGuCzb7xTgM8BBQDPwAHBpjPG5bP04YC5wDnAxcDjwFPB2YBfgB8BE4K/AecXjdiKtQ2OMZ2X/DwCuAc4EVgHf3MaXo6I8M3c+Y0aPZkD/lpVEB+w3HoA5c+ez+/Bdt+kcc+bOI5eDMH6fFst323UYuw/flTnPzdum46t89hie48WlbVspLVyS5/D9q9htaI6XV7Y/FEBdDey1W45/zGzmDZOrec0B1dTVworVcNfDTTw1b/NxRw+vYmMTLH2l5bEWLs1DDkYPz/H8y46X3VeM3rWKRcvavl8LlxaYvB8MH5pjyRbyTVFtDXzq3Fpqa2BDAzw5L89djzTTWNJgtro6x/qGtscpbrPHrlWsWG0ru97MMkrbwjJK3WEZpR1lyCEH8MpjM9osX/nQE4y98G0M3H9v1jz9DEMOmQSFAq882nLbhsVL2LBwMUMPmbSjkixJUp9nDxB15FJS8OLHwChgNLAGuBt4BDgMOAUYAdxYst9AUsDhMGAKKQhyazvH/y/gCuBQoAn4JfBVUlDkWGBCtr47vkEK0LwZOBk4IUuPgGUrVjJ8113aLB8+bBcKBVi6fMV2OUfxmO2dZ+mKbT+HymPwgByr17ddXlw2eMCW9x0+JAc5OHjfag7br5o7HmrixvuaWLuhwDkn1DBhj81NLAf3hzXr21YSrF6Xfg8ZYOvavmTwAFjdzvtZfI+H9O94/9Xr4P6n8tx6fxM3TW9m1oI8R4QqzjuppsX4h0tfKTByWI7aVk08xo1MWw0ZuC1XoR3BMkrbwjJK3WEZpR2l3+gRNCxe0mZ5w6Ilm9YD1I/aHYANi15us+2GxUuo32NED6ZSkqTKYg8QbVGMcVUIYSOwLsb4MkAI4dPAozHGzxa3CyFcBDwfQpgQY3wmxthiMvJs/cshhEkxxqdLVn09xjgt2+Y7pADIlBjjP7Jl1wFTu5ruEMJA4F3AuTHG+7JlU4GFXT1WpWrY2EhtTduPf11dLQAbN277uNUN2TFqa2vbnqe2lnUbNmzzOVQeNdXQ3M5Q2I1NBchBbXWO1GGsrSyL0b8ervl9Iy9krS1nLcjzn2+r44RDanjmxcZ0npocze00gmxq3pwO9R011Wz5/cxBO7ekFu5+rDTTFZgxH5atKnDiodVMGlfFjGy4m4diM2FMDeccX8O0R5vZ2FTgqInV7DE8VS5taTgb9R6WUdoWllHqDsso7ShV/evJN7Qtx/IbGiCXoyrr/Vid/c43NLa7bc1go2WVpJC3x6Ak9SQDIOqqg4EpIYTVrZYXgPHAMyGEYs+No4DdSD2NCsBYoDQA8mTJ3y9lv59qtaw7TVvGA7XApgG8Y4wrQgixG8eqSPV1tTQ2tZ1kcePG9IBdV1e3Hc6RjtHY2PahfWNjI/V1bSud1LtU5VIlUKl1G1JlQHU7X9Bra3JQgMbmLT/AF4d4WLGGTRVLxeWzFuQ5eN8qilVTTU0FqtsZdLtYqdS09flIVQYd5pt2+p3WVAMFaOeWtFUPPJ1nyqHVjN8jx4xsrutnXixw+4PNvOGwat53eg3kYPkqmPZoMycfXk079QjqZSyj1BmWUeoOyyiVW359A1X1bcuxqn71UCiQX58C8M3Z76r6tuVRVb96mtc39GxCJUmqIAZA1FWDgN8BnwBaf+tblP3+A2mOj4uAF0kBkBlA6ye90kf8whaWOUxbDxg+bBeWLl/ZZnlxSJDddh22Xc5RPGbrsdqXrVjJpGwsd/VeY0fmuPDU2vRJzGp8vnHTRlavKzC4naEgisuKw3+0Z/W69FFvb9iQtesLVFelMbQ3NqXhSvYZ3bZyqTh8yap1tpTqjcaOyHHBKTUt8s3VtzSyeh0M7t/2/RyULVvVzpA1W9OUh/UN0L+u5XEfinkeeybPqGE5mvKweHmByftVQSG1yFXvZhmlzrCMUndYRqncNix6edPwVqXqR7cc8qo4TFa/0SNoeLHlMFj9Ru3Oyn8+0cMplSSpchgA0dZsBErb0T0KnAXMjzG26SgeQtgV2B+4MMZ4f7bs2HaO25NP98+S5hQ5imzYqxDCsCxd9/XgefuMCfuM47EZM1m3fkOLSWZnzH6GXA7222fcdjlHoQCznnmOiRP23bR86fIVLFm2nP1OOXGbz6GetWhZgevvaNkUcc16WLS8wLiRbWOTY0ZU0djcdkLYUqvXp2O0Nzb6kIE5mppTxVI6f57J+6UJa0uPOWb3VEnQ3mSlKr9FywvccFfLprJr1sPiFXnGjmgn3+yeo7EZlnWQb7akrgYG1MPadiaUbWpOk9cWjd8jncdJiXs/yyh1hmWUusMySuW26vFZ7HrM5DbLhx11MM3rNrB29rxsu5mQyzF08kG88sjmQRLqR+1Ov71G8cr//N+OSrIkSX2ereu1NfOAo0II40IIw4H/BnYF/i+EcHgIYd8QwikhhP8NIeSAFcAy4D0hhPEhhCmkCdFbP823NzPkdpktMsa4FrgO+HoI4fUhhIOA60mTsQs44bVH0dyc53d33bNpWWNjE3+65y9M2n/CptawLy1dxvMvvNitc+wzZi/G7jma3/35XgqFzW//b++YRlUuxwmvPWLbLkI9rqERnltUaPHTnIen5uUZ1A8mjdtchAyohwP3rmLm83lKh7AdNjj9lHryuWaGDoR9S1rODqiHiWOqeHbR5rhq8VhHTWxZVB05sZpV66wk6K0aGmHu4kKLn+Y8zJhXYFA/OGBsy/d90rgqZi1olW8GpZ+i6qpUkdTaCQen+PycF9oZuL3EmN1zHDC2ikfn5DdVXqr3soxSZ1hGqTsso7Qj1Y/cjYH77wNVm+8Ti2+5k/qRwxl1xhs2LasdPoxRZ53CS3+4h0I23tqamc+yZtZzjL3o7BbHHPe+cynk8yy+9a4dcxHaIQr5Qp/+kaTezh4g2ppvAD8hzd3RD9gHOAa4CrgTqAfmA3fEGAsAIYRzgO+S5viIwCW07XnRXim5PUvOjwMDScN1rSYFkmIIkQAAIABJREFUYYZsx+P3aZP2H8/rjz6S//n5r1nxyivsOWokf7r3L7y0ZCmfvPg9m7b70rd/yONPz+Ivt/x807K169Zx8+13kiPHk7NmUyjAzbffxeCBAxg0cABnvenkTdt+YOq5fOor3+Ij//UVTjz2tTw3fwG3/unPnP6G1zN2zz126DVr+5kxN8+CSQXeclwNI3dpZm1DmsAzB9zzWMs444VvrKMAfPOmzZM9Tn+imVftU825U2r5+4xmNjTCkaGKqiq46+HN+69aB3+f0cyxB1VTU51j4ZI8k8ZVMW5EjhunW0PQ1zw9P8/CpVWceUwNI3ZpZl1Det9zObj3Xy3zzQWn1FIowLdvSa27B/WH97+5lifn5je1tN5vzyom7JljzsICccHm4mPoQDj7+Brigjyr18PIXXIcvn8Vi5cXmPaYcfC+wDJK28IySt1hGaWuGvf+c6kdOoR+e44EYOTpU+i/12gA5n7/ZzSvWUv48sfY6x1ncM+EKWxYkEaLXvSbO9jnkvN59bVfYdCk/di4bAXj3vcf5KqqmH3F91qcY9blX2PyLT/gqDuu58Ubb2fwQYFx7z+XBdfdxNrZc3fsBUuS1IcZAFGHYoxzSAGP1t7awT73AAe1Wlxdsn4+LYfVIsY4vZ1lNwA3dDKd72z1/1pgavZT9M3OHGtn8ZlLP8C1v7qJu6bfz+o1axm/9xi+9pmP8+oDwqZtcjmoyrXsmLN6zVqu+9VvKC7O5eDG3/8RgJG779aicunoww/lS5d9mOt/fQvfufan7DJ0MOe/7Qymvu3Mnr9A9ZgCcMNdjZx6ZA2vmVRNbQ0sXFLg5r80thm7ugAUWoU2126AH92+kVOPqOHoA6upqkotZW+c3sjLK1tufOfDWSXExGoOnVDFslUFbpzexJNzO25Nqd6nAPxsWhOnHF7NURNTvnlhaYHf/K2J5avb2bjEho0QF+QZP7qKQ8an+87yVQWmPdLM/U+3zAsNG9MY/0dOrKZ/XaqkfGBmnr882bxpgmP1fpZR6i7LKHWHZZS6at+PXEj/sSngQaHAqDPesKlXx8Jf3EbzmrVQKFDIt7ofFAr88/R3c8BVn2DvD76D6v79WPnQEzz+zstY98z8Fpu+/KfpPPK2i9n/sx/kwKs/Q8OS5Tzz5R8y58of7IhLlCSpYuQKrZ/6JZUqvPz0w+VOg/qQEZMO59P/21DuZKiPufJd9Xzuho1b31AqccXUOiyj1BWWUeoOyyh1xxVT67i9Nmx9QylzWmOE7TQsdl9zxgdm9+mKud/+YP+d8n2T1HfYA0R9QghhNam9VeuCtQCcWpxwXZIkSZIkqa+wYbIk9SwDIOorDu5g3Qs7LBWSJEmSJEmSpD7BAIj6hBjjc+VOgyRJkiRJkiSp7zAAIkmSJEmSJJVBPp8vdxIkqaJVlTsBkiRJkiRJkiRJ25sBEEmSJEmSJEmSVHEMgEiSJEmSJEmSpIrjHCCSJEmSJElSGRTyhXInQZIqmj1AJEmSJEmSJElSxTEAIkmSJEmSJEmSKo4BEEmSJEmSJEmSVHGcA0SSJEmSJEkqg0IhX+4kSFJFsweIJEmSJEmSJEmqOAZAJEmSJEmSJElSxTEAIkmSJEmSJEmSKo5zgEiSJEmSJEllUMgXyp0ESapo9gCRJEmSJEmSJEkVxwCIJEmSJEmSJEmqOA6BJUmSJEmSJJWBQ2BJUs+yB4gkSZIkSZIkSao4BkAkSZIkSZIkSVLFMQAiSZIkSZIkSZIqjnOASJIkSZIkSWWQL+TLnQRJqmj2AJEkSZIkSZIkSRXHAIgkSZIkSZIkSao4BkAkSZIkSZIkSVLFcQ4QSZIkSZIkqQwK+UK5kyBJFc0eIJIkSZIkSZIkqeIYAJEkSZIkSZIkSRXHAIgkSZIkSZIkSao4zgEiSZIkSZIklUEhny93EiSpotkDRJIkSZIkSZIkVRwDIJIkSZIkSZIkqeI4BJYkSZIkSZJUBoV8odxJkKSKZg8QSZIkSZIkSZJUcQyASJIkSZIkSZKkimMARJIkSZIkSZIkVRznAJEkSZIkSZLKoFDIlzsJklTR7AEiSZIkSZIkSZIqjgEQSZIkSZIkSZJUcQyASJIkSZIkSZKkiuMcIJIkSZIkSVIZ5POFcidBkiqaPUAkSZIkSZIkSVLFMQAiSZIkSZIkSZIqjgEQSZIkSZIkSZJUcZwDRJIkSZIkSSqDQj5f7iRIUkWzB4gkSZIkSZIkSao4BkAkSZIkSZIkSVLFcQgsSZIkSZIkqQwK+UK5kyBJFc0eIJIkSZIkSZIkqeIYAJEkSZIkSZIkSRXHAIgkSZIkSZIkSao4zgEiSZIkSZIklUGhkC93EiSpotkDRJIkSZIkSZIkVRwDIJIkSZIkSZIkqeIYAJEkSZIkSZIkSRXHOUAkSZIkSZKkMijkC+VOgiRVNHuASJIkSZIkSZKkimMARJIkSZIkSZIkVRwDIJIkSZIkSZIkqeI4B4gkSZIkSZJUBoV8vtxJkKSKZg8QSZIkSZIkSZJUcXKFQqHcaZB6Mz8gkiRJkiT1vFy5E1AOx755ep+ud/jb74/fKd83SX2HARBJkiRJkiRJklRxHAJLkiRJkiRJkiRVHAMgkiRJkiRJkiSp4hgAkSRJkiRJkiRJFccAiCRJkiRJkiRJqjgGQCRJkiRJkiRJUsUxACJJkiRJkiRJkiqOARBJkiRJkiRJklRxDIBIkiRJkiRJkqSKYwBEkiRJkiRJkiRVHAMgkrZZCOHeEMK3yp0O9S47a74IIcwNIVxS7nT0ZTtr3umKEML1IYRbyp2OHSmE8PkQwmPlTod6l501X+yM94DtbWfNO10RQpgaQlhR7nSopc58/n2WkiRps5pyJ0CSpL4ohDAV+HaMcVirVYcDa8uQJFWgEMI4YC5wSIzxiXKnpxcolDsB6pUqNl90cA+4BMiVJ1UVpWLzTleFEOYCV8cYv9tq1U7zGoUQ7gUeizF+tNxp2Qo//5IkdYE9QKReKIRQFULwobYLQgi15U5DTzNfdF138kUX9snRTqVAjHFZjHFDV8/bk8w7Xdcb7ilZGtrNZyqfEEJ1udOg3qer+WI7lDWrY4yrunJO9U7lvqf0hvJOXePnX5KkrrEHiNQJIYRTgM8ABwHNwAPAJTHGuSGE+4G/xBg/WbL9bsCLwJQY499CCHXAl4H/B+wCPAlcHmOcnm0/Ffg2cD7wVWA/YEIIYUS236FALfAv4CMxxsdKzhWA64DJwLPApcCfgTNijL/LttkL+CZwMpAH/gpcGmOc34lrvz5L82PAh4B64JfAxTHGpi3s844sHYHUEv4e4MMxxiXZ+jnAD2OM3yrZ5xDgUWBCjPG5EMLQLM3/lp3zIeCjxdaPIYTPA2cA3wc+DYxlK/e0LbyPl8YYnyvZ5mjgv4GJpPfpSuBWSlpehhAOAr4GnJCdswCsAf6O+aIv5ot7gaeAJuAdwBPAiSGEjwDvBPYFlgO/Bz4eY1wXQjge+F+gEELIk/LAF2KMV7RuQZmtfzdwGnAK8ALwc+ANbM6LzwG7AaOBBtJ7cRqwS4xxlXmn9+WdEMJppPdx1xhjIYRwcHZNX40xfirb5lqgLsZ4fvb/W4AvABOARcD3WqV5Lum13w/4d9K9Zyopf/0rvTXcF2OcUrLPx4CPAXXA/5Heh+YtpbvVNbwV+FyWnnXZ63UGcDutWsCGEG4FVsQY31WS1muB/YGzgGXAxaT76rXAiaR8/a4Y4yOdSEsxz14AfB0YA0wHLooxLtzCPofTQX4OIVwHjIgxvrlknxrSZ/DyGOP1WWDwctJndBQQgS/FGH+TbX88cC/wJuBLpM/sycBfOriWfYFvAa8BBgIzgU/GGO8u2WYU6b1+PSkvfCa7ltJ7R4d5tqeYL3osX7R7j+nEs8lztHMPCCH8BBgSYzwrO/69pPJrA3ARsBG4Jsb4hZI0bPX+vi3MO9s/74QQhpCeQY6MMT6aHX8ZMCvGeHS2zTuAL8cYx2b/HwR8B3gt6X34DenesTZbXyyHHwI+CGwIITwPjAOuDiF8GyjEGKtL0nFy9nqOAf4GXBBjfGmLb0AflL0uxwOvCyF8mPS52wcYQnruP470DHIXKV8sy/br8DNc0ovrHFKePpz03Pl20vvwA9L3jr8C5xWP24m0Di35/A8ArgHOBFaRyg5JkpSxB4jUOQNJD5KHAVNID7e3Zut+QaqELPX/gBdijH/L/v9v4CjgbOBVwE3An0II40v2GQB8ArgQOBB4GRgM/AQ4Ott/NvDHEMJASK26gduA1cARwHtIFfabWgpmX8zuBF4BjsmOtRq4I1vXGSeSHsyPz67tLODzHWxfQ/oi8GpSJd647DqK/pdUsVzqncD0ki/8NwPDSRXGh5G+RE8LIexSss+ELC1nAod04jo6eh8JIQwGfgc8Tvry+1ngKlq+nkOBu4FHgI+T3rN/AHMwX/TVfAEpUNBAeh3ely1rJn1RnZStfz3pCzCkYNeHSV8yR5ICF9/o4PifI1VOvwr4I6ly5JrsGs4jVUYNJL02NwOn0rLFr3mn9+WdvwKDSPcKsmtZQgqMFr2OVNFFCGEy8GtSsOcg0vV+MYRwfqvjfoxU6XYocAVwJKkF+BRSZdpZJdtOIQXoTiDl0Quyn63KKuB/SapYLL4Xt9C1ITU+THodDgH+APwMuCH7fSipgvWGLhxvAPApUiDyaFLF0K862L7D/Ey6tlNCCCNL9nkz0J/0eaTkfO8hfdavBn4WQjiu1bm+AlwGHECqZO7IIFKF7+tJr82fgN9lgcOin5Hez9cBb8nOv3ur43Qmz25X5osezRfQ/j2mw2cTtnwPaK9n2PmkBhlHku7/nwshnAidu79vC/NOz+SdrJX/Y2wuW15FanhwaFbpDek+ch9sqgi/kxQkmQy8FTgJ+F6rQ59ICjadBJxOypMLSc++o0jPNUUDSWXT20lBgLF0/MzTV11KCl78mM2vwRo2P/cfRrofjwBuLNlva5/hov8ileuHkhrd/JLUSOVi4FjS/eGKbqb9G6T35s2kgNoJWXokSRL2AJE6JcbYYpK5EMJFwMshhEmkB+BvhxCOiTHen23yH2RfsEIIY0kVUmNijIuz9d8KIZxKqqD7TLasBnh/jPGpklPd2+q87yO1HjqeVIl6Mqll0nElLaE/TWrNV/T/gFyM8T0lx7kQWEF6OJ7WiZegAXhnjLEBmBlC+BypIviz7W0cY/xJyb/zslZUD4YQBsQY15G+XH4hhHB4jPHhrNL0P4CPZuk7ltQ6akSMsTE7zidCCGeSvshdmy2rJbWUWt6Ja+jwfYwxPk36YpcH3hNj3AjMCiF8Hfifkt0+BDwaY/xsyXFuBp4nfVl+0HzRt/JFZk6M8fJW6S0dA/v5EMJngR8CH4oxNoYQXiG1kFzSieNfH2O8MbuOT5HGbl6S9RZ6L6mF+KQs7ZfTtjLfvLNZr8g7Wc+cx7NrfjT7fTXw+awCahipMmN6tstHgGkxxi9n/z8TQjiQFEj9acmh744xXl38J6QeRADLY4wvt0rGclJ+LACzQwi3kyq1rtta+kkVO9XArTHGBdmyGdk5O7E7ALfHGK/N9vki8AHgnyUtna8C/h5CGNFO2ttTA3wwxvhwtv9UUv44vLisVIyxw/wcY3wghDCbFGQsVtZdANwUY1wfUk+qTwInxhgfzNbPyyoq30uqiC36bGkPjo5kPTRKKzQ/H0I4i9ST4wchhImk92lyScvyi0iB9OK1dDbPbm/mix7KF5k295hOPJsUy5j27gGtPRFj/GL297MhhA+R8trddO7+vi3MOz2Xd6aTyphvZb/vIgWZjs3+PoHUYAfSs2w9cH5Mw3HOzPLB70MIl5U8s6wh9YbZ1PMyhNAMrGnnta0B3htjnJdt9322UF73ZVm5vhFYV3wNss9I6+f+i0jPhRNijM904jNc9PUY47Rsm++QAiBTYoz/yJZdR+r12SVZgO5dwLkxxvuyZVNJAS1JkoQBEKlTQgjFFjlHkYapqSK1mBsbY3w6hHAX6QvH/SGEfUhdzt+d7X4Q6Qvh7NByDP46YGnJ/xtbVVQS0nA1V5K+eI3IjtOf1PIKUsutBa0qYP/ZKvmvBvYLIaxutbweGE/nKisfzyoqix4ABoUQxpR8yS1N92RS6+aDSZWAxd5mY0ld9heFEP5Ielh/mFQpVEdq7VpM82Bgeasvzf2yNBfN70old0fvI/A06fV8Igt+FP2Tlq0XDwamZK9njvQ6Vmd/T8d80efyRabNcBohhJNIwYiJpOEPaoD6EEK/2PU5Pp4s/hHTEFprgP8KIfyQdP0FWuadR0iVn5h3enXeKa2UOo6UX84mVUoNJ/XaKfZAOQD4bav97wcuDSHksiAGtJMXOzCjZD9IQykd1Ml9HydVij4VQriTVIl2c4xxZRfOX5qvX8pe29I89xLp3jiC1ANpa5pKKyVjjDGEsJL02rWprOxEfoYUKHg38I2QWm2fyuaW1BNILcT/3OqzVEsKahUV6ML7klVGfYE0xM1o0r2jHy0/Z42xZOi5GOOzIYQVJYfpbJ7d3swXPZQvMm3uMZ14NumK1j0JFpFeA+jc/X1bmHd6Lu9MB96VHfN4Ug+PxcAJIYQns/MWgzcTSWVs6XPK/aR8FdgcUHsybmHYyXasKwY/MqX5qtKVPveXKpDuxc904TP8ZMnfxeHDWufv7ryu40l5bNPnOca4IoQQu3EsSZIqkgEQqXP+QBq79SLSOPxVpFZtddn6XwDfCSFcDJxLqkQvPvAOInVzPozUu6DUmpK/17dz3p+SKvsuJvUwaCANt1TXzrZbMoj0Je9c2g5D0JmW612StXy+gzTkx7nZOcZly0rTfS3w05DmWbgA+HXJl7VBpNf5+HbSXPpFem0Xk7e197EzBpGGyfoEqaJ3AamHyEuk4QYexXzRRi/PF232CWm85t+Thpr6FKml/XFZ+upIY6x3RWOr/wdmPxeRWouuIg1rUnwtppOGKKnCvNOb8859wDtDmv9jY4xxdghhOmnoo2Fs7v3RFV1JQ+t8VaCTw5vGGPPAySGE15Jahl8MfCmE8BpSvmr9OrU3SW7r87deVgzO9NSQq53Jzz8FvhJCOIoUmHouxvj3bN2g7PebSPmjVEOr/7vyvnyT1Or+Y6Qhe9aTxuDv6uesM3l2uzJfAD2XL7a0/fZ4Ninq9j1hW5l3gJ7LO38hBUQnk4a7+iTpufNyUtCrNNjeWdta1nRlaLO+rPS5v/U1L8p+d/Yz3F5ebr3MIcolSeoBBkCkrQgh7EpqNXdhzIYyCmloilK3AT8itQL7D1qOT/wYqQXZyLh5KKTOOpo0hM2d2XnHkFoWFUVgTAhh95IWfUe2OsajpBbJS2KMa+ieg0MI9SUttl9L6iLfpqU2qeXZrqQJX1/I0t06TZCG21lLGt7gjaTK5dI0jwKaY4zPdzPNLXTyfYzA20MItSVDjhxJy/GxHyVVVK8iDSVxvvmi7+aLDkwmDfP0n8UFIYTWc7psJL2HXZLlxSrgdzHGe0OaWPStrTYrtsp8A+ad3px3/krqHfQRNgc77iNVSu1Cy0lIZ5LmTCl1LDC7VS+O1oo90rqc1zojxvgA8EBIw83MJ01YvISS8d9DmjvgINLk813VlfkFakLJ0DQhNf/ehS23gt9afibGuDyE8FtS76DXAteXrH6aVCk5Lm6eX2d7OBr4ScwmlQ4hDAL2Lk0W6VoPjZuHwJpAqngt2pH3uzbMFz2SL9ro5LPJ9roHdOb+vs3MO9s/78QYX8l6enyIzcH2JaR5pU6nZbB9JjA1hNA/xlhsCHEsaV6KrfUI6NZzTYVp/RoUn/vnZ0G+Fjr5GYbtNNfOFjxLem48imzYqxDCsCxd9/XgeSVJ6jMMgEhbt4LUsv89IYTFpJbHX6HkQTYb0uY24IukyrpflaybE0L4Jall8n+SKi9HkCbJezzG+KcOzj0HOC8bDmcoaYz8dSXr/ww8lx37E6SKuC+yeTgdSL0Q/hO4LYTwedKD8d6kyQ6vijG2bqHWnjrguhDClaRK//+i7WSKRc+TvjxcEkK4hjRZ42dabxRjzIcQbiC9lrPj5rGSiTFOCyE8APw2hHAZaRLKPUkt6m6JMT7a+nidsNX3kTQW75XAj0MIX822+Vi2rrjdf5NaeP2QNAn0R0P4/+3dXailVRkH8L9TE0SEJEQZWRjJgm6Kboq0qLwovEkkDAkxusg+oEBKgqzIJCLHLL2JwgkLLQsio09CMpMCpS40tEWZQzGYYo75kQbF6eJ5j7PnzJ45x3POnjl7ze8HXjh7z3vevfcz73n3+q/1rHZGqkf0KVEXy1YXR/KXJLtbax9NrQQ5K9W/e9a+VNumt6daf/x7ZrDhaA6kPoe3ttq0/K7URqhJcmpr7fxU67SV1HukdnZo7fTeH22t3ZX6vD4y/fFtqT2AnptDB6WuSnJHa+2y1KDVm6a/88F1fsxDqRUE72yt7U/ydK9NcbdkCpHOTrWpeSjJG1MDffemauKq1to5qYGVS1KDhpvxbGYJ/zfJta21j6UG665N8tve+5FaxaxXz6uuS83Q3ZWZMLH3/kRrbU+Sq1trz0ly+3ScM5P8q/f+7U28htXzOq+19uPp/y+fPcbUhueW1O+aD02ve8907ivTc47l9e4Z6mKhdTHPRu5NtusasJHr+6apnYXXzq2plSnfn37Wgdbavan7zw/PPO+G1O/U61trn0vdH1yT5Ft9/T3L9iV5S2vtpiT/6b3/cxPnuez2JXlDq5XAT+Tgff93W2tfSq0IXr3vX93/bL1/w8n8z3xbVtH03p9stX/Ila21R1KB4xWpmgcAYoklrGuamfue1Izwu1ODWB+f89QbUj27b+u9r9107n2pJfN7kvwpyQ9S/f3Xm9X5/tSM0N+nvmB9NTM9j6eZSO9KtdK5I9WK6YrUDfXT03OeSi2X/1uqBcc9Sb6R6te/0S/Qt6S+FN6WGoj9Yaq/+arZQf+Hp9f77tTy70tzMERY67rUQOjeOY+dM/28vakZazemeuk+OOe569rI59h7fzw1k+61qUHlz+fg61x9Px9IfZHdNf137nSOr197vIm6yM6ti7XnOXO+d6UGaC5N1csFqVn9s8/5XZKvpQazH0ptZj3veCtr/t5K6kv16dOxL52OfVJqkOLi1Oe1kpohq3Z2bu0kFXLsyjTLsvd+IPWePNB7f2ZT62mm//mp69DdqQGqy2YGxJL5tfi/1KDXxUn25/B9RDbrsdTn+JPU+3F5kkummc97U/Vx/fS67svhM7XnDZhu9M+O5MnURr43plbXPJZk7cqrWUet51W9Np19IMnPe+//WPPYp1PX+k+mPrefpWrl/k2+hqSuHQdSffdvTrVrWxtYXJjq4f/r1L+jr6euC7Pt9RZVs0ejLrKwujjMBu9NNnoNOOr5bOT6vkVqJwutndXfNbMbtd+amd8/089/Ksk7UpNy7kgF8r9M1dB6PpOa0HBfNrbHyoj2pIKDe1Lvwe4cvO//RWrSypeTHOi9rzyL74lbreX1fCJV4z9KhZC/ybPfowgAhnXSysoiV2MCx1pr7czUgMmre+/3r/f8DRzvm0lO7r2ft+WTO/zYb059KTttA7PSjovW2ntTg6on90M3bV4q6mK5tNY+leQDvfdX7oBzUTssXGvtoiRX995PWcCxX5AaOL6o937zdh9/O7TWXp4KFc/uvf9qveefKE70uli07b6+7yRqBwCAVVpgwZJrrZ2bmjX659SS7K8kuX0nf5FtrT0vtST/s0m+t5MGKltrF6ZaROxP8rokX0xtprxU4Ye6WC5TG5w7U20UzkrNHrzmOJ2L2mEIrbWTkrw4tWLoQKqd3Y7QWntbanPdu5O8LNVq56+pwWgWaCfXxaIt4/V9JzmRawcAYJkJQGD5vTC1vP+0JA+nZj/PW3o9V2vt8dQS7LV9aFdSm3cvwgWpVRV/SLUB2bJpo8p7cuTX8po5bYTmeWmqbcNLUu0Nbsqc/QaWgLrIttbFop2RqrMXpWaBX5kK344HtZOlqp2l0Fr7aQ7dmH7VSpIvpK632+0VqbYzf0/N1D5sA9vNaK39MdXnfa2VJBf33r8z57G1dqde9+lJHk+1y7pgand0wlAXx9yWru87idphZOvdS61uuA4AbIwWWHCCa6296igP71+WlQ/TZpNHaxe0b7u+qJ4I1AWbpXaYp7V2apLnH+HhR3rvjx7L89mKKRzbfYSHH+y9P3ksz2eZqQs2S+0wslHupQBgpxCAAAAAAAAAw9l1vE8AAAAAAABguwlAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QjDZLQlAAABW0lEQVRAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4QhAAAAAAACA4fwfDT58itGFXKgAAAAASUVORK5CYII="
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(featurestore.visualize_featuregroup_correlations(\"players_features\", plot=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also override default parameters and configure the plotting options:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(featurestore.visualize_featuregroup_correlations(\"players_features\", \n",
    "                                                 featurestore=None, \n",
    "                                                 featuregroup_version=1, \n",
    "                                                 figsize=(8,6),\n",
    "                                                 cmap=\"coolwarm\", \n",
    "                                                 annot=True, \n",
    "                                                 fmt=\".2f\", \n",
    "                                                 linewidths=.05, \n",
    "                                                 plot=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Descriptive Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_player_age</th>\n",
       "      <th>average_player_rating</th>\n",
       "      <th>average_player_worth</th>\n",
       "      <th>metric</th>\n",
       "      <th>sum_player_age</th>\n",
       "      <th>sum_player_rating</th>\n",
       "      <th>sum_player_worth</th>\n",
       "      <th>team_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.340000</td>\n",
       "      <td>150.96327</td>\n",
       "      <td>142.29665</td>\n",
       "      <td>min</td>\n",
       "      <td>2434.00000</td>\n",
       "      <td>15096.327</td>\n",
       "      <td>14229.664</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>count</td>\n",
       "      <td>50.00000</td>\n",
       "      <td>50.000</td>\n",
       "      <td>50.000</td>\n",
       "      <td>50.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.522913</td>\n",
       "      <td>1187.08750</td>\n",
       "      <td>1285.64720</td>\n",
       "      <td>stddev</td>\n",
       "      <td>52.29132</td>\n",
       "      <td>118708.750</td>\n",
       "      <td>128564.730</td>\n",
       "      <td>14.57738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25.568400</td>\n",
       "      <td>717.38370</td>\n",
       "      <td>734.51310</td>\n",
       "      <td>mean</td>\n",
       "      <td>2556.84000</td>\n",
       "      <td>71738.375</td>\n",
       "      <td>73451.310</td>\n",
       "      <td>25.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>27.000000</td>\n",
       "      <td>7191.86330</td>\n",
       "      <td>7920.43260</td>\n",
       "      <td>max</td>\n",
       "      <td>2700.00000</td>\n",
       "      <td>719186.300</td>\n",
       "      <td>792043.250</td>\n",
       "      <td>50.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "desc_stats_df = featurestore.visualize_featuregroup_descriptive_stats(\"players_features\")\n",
    "desc_stats_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training Datasets\n",
    "\n",
    "To group data in the feature store we use three concepts:\n",
    "\n",
    "- Feature\n",
    "- Feature group\n",
    "- Training Dataset\n",
    "\n",
    "Typically during the feature engineering phase of a machine learning project, you compute a set of features for each type of data that you have, these features are naturally grouped into a documented and versioned **feature group**. \n",
    "\n",
    "In practice, it is common that organizations have many different type of datasets that they can extract features from, for example if you are building a recommendation system you might have demographic data about each user as well as user-activity data. \n",
    "\n",
    "When you train a machine learning model, you want to use all features that have predictive power and that the model can learn from. At this point, we can create a training dataset of features from several different feature groups and use that for training. That is the purpose of the training dataset abstraction. \n",
    "\n",
    "Of course you can always just save a group of features anywhere inside your project, e.g as a csv, or .tfrecords file. However, by using the feature store you can create **managed** training datasets. Managed training datasets will show up in the feature registry UI and will automatically be versioned, documented and reproducible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once a training dataset have been created you can find it in the featurestore UI in hopsworks under the tab `Training datasets`, from there you can also edit the metadata if necessary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get Training Dataset Path\n",
    "\n",
    "After a **managed dataset** have been created, it is easy to share it and re-use it for training various models. For example if the dataset have been materialized in tf-records format you can call the method `get_training_dataset_path(training_dataset)` to get the HDFS path and read it directly in your tensorflow code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"><span class=\"ansired\">Out[</span><span class=\"ansired\">32</span><span class=\"ansired\">]: </span>&apos;/Projects/demo_featurestore_admin000/demo_featurestore_admin000_Training_Datasets/tour_training_dataset_test_1/tour_training_dataset_test&apos;</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_training_dataset_path(\"tour_training_dataset_test\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default the library will look for the training dataset in the project's featurestore and use version 1, but this can be overriden if required:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"><span class=\"ansired\">Out[</span><span class=\"ansired\">33</span><span class=\"ansired\">]: </span>&apos;/Projects/demo_featurestore_admin000/demo_featurestore_admin000_Training_Datasets/tour_training_dataset_test_1/tour_training_dataset_test&apos;</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_training_dataset_path(\n",
    "    \"tour_training_dataset_test\", \n",
    "    featurestore=featurestore.project_featurestore(),\n",
    "    training_dataset_version=featurestore.get_latest_training_dataset_version(\"tour_training_dataset_test\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Dataset Visualization\n",
    "\n",
    "Just as for featuregroups, the training dataset statistics can be visualized in the Jupyter notebook in `%%local`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(featurestore.visualize_training_dataset_distributions(\"tour_training_dataset_test\", plot=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also override default parameters and set plotting configuration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(featurestore.visualize_training_dataset_distributions(\"tour_training_dataset_test\", \n",
    "                                                  featurestore=featurestore.project_featurestore(), \n",
    "                                                  training_dataset_version=1, \n",
    "                                                  figsize=(12, 9),\n",
    "                                                  color='lightblue', \n",
    "                                                  log=False, \n",
    "                                                  align=\"center\", \n",
    "                                                  plot=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature Correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(featurestore.visualize_training_dataset_correlations(\"tour_training_dataset_test\", plot=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can also override default parameters and set plotting configuration:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(featurestore.visualize_training_dataset_correlations(\"tour_training_dataset_test\", \n",
    "                                                 featurestore=None, \n",
    "                                                 training_dataset_version=1, \n",
    "                                                 figsize=(8,6),\n",
    "                                                 cmap=\"coolwarm\", \n",
    "                                                 annot=True, \n",
    "                                                 fmt=\".2f\", \n",
    "                                                 linewidths=.05, \n",
    "                                                 plot=False))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Descriptive Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_attendance</th>\n",
       "      <th>average_player_age</th>\n",
       "      <th>metric</th>\n",
       "      <th>team_budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1038.5237</td>\n",
       "      <td>24.340000</td>\n",
       "      <td>min</td>\n",
       "      <td>760.8729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.0000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>count</td>\n",
       "      <td>50.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14232.3090</td>\n",
       "      <td>0.522913</td>\n",
       "      <td>stddev</td>\n",
       "      <td>5238.9430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8669.3940</td>\n",
       "      <td>25.568400</td>\n",
       "      <td>mean</td>\n",
       "      <td>8723.2920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>92301.0860</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>max</td>\n",
       "      <td>21319.5330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "desc_stats_df = featurestore.visualize_training_dataset_descriptive_stats(\"tour_training_dataset_test\")\n",
    "desc_stats_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since descriptive stats is just a pandas table and not a matplotlib figure it does not need a DISPLAY to work, so you can run it from the spark driver or executor as well:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"max-width:1500px;overflow:auto;\">\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>average_attendance</th>\n",
       "      <th>average_player_age</th>\n",
       "      <th>metric</th>\n",
       "      <th>team_budget</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1038.5237</td>\n",
       "      <td>24.340000</td>\n",
       "      <td>min</td>\n",
       "      <td>760.8729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50.0000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>count</td>\n",
       "      <td>50.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>14232.3090</td>\n",
       "      <td>0.522913</td>\n",
       "      <td>stddev</td>\n",
       "      <td>5238.9430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8669.3940</td>\n",
       "      <td>25.568400</td>\n",
       "      <td>mean</td>\n",
       "      <td>8723.2920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>92301.0860</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>max</td>\n",
       "      <td>21319.5330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "desc_stats_df = featurestore.visualize_training_dataset_descriptive_stats(\"tour_training_dataset_test\")\n",
    "desc_stats_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get Featurestore Metadata\n",
    "To explore the contents of the featurestore we recommend using the featurestore page in the Hopsworks UI but you can also get the metadata programmatically from the REST API"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Update Metadata Cache"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"><span class=\"ansired\">Out[</span><span class=\"ansired\">40</span><span class=\"ansired\">]: </span>&lt;hops.featurestore_impl.dao.common.featurestore_metadata.FeaturestoreMetadata at 0x7f881ba8fd30&gt;</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_featurestore_metadata(update_cache=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List all Feature Stores Accessible In the Project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"><span class=\"ansired\">Out[</span><span class=\"ansired\">41</span><span class=\"ansired\">]: </span>[&apos;demo_featurestore_admin000_featurestore&apos;]</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_project_featurestores()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List all Feature Groups in a Feature Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"><span class=\"ansired\">Out[</span><span class=\"ansired\">42</span><span class=\"ansired\">]: </span>[&apos;games_features_hudi_tour_1&apos;,\n",
       " &apos;games_features_1&apos;,\n",
       " &apos;teams_features_1&apos;,\n",
       " &apos;games_features_on_demand_tour_1&apos;,\n",
       " &apos;attendances_features_1&apos;,\n",
       " &apos;players_features_1&apos;,\n",
       " &apos;season_scores_features_1&apos;]</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_featuregroups()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default `get_featuregroups()` will use the project's feature store, but this can also be specified with the optional argument featurestore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"><span class=\"ansired\">Out[</span><span class=\"ansired\">43</span><span class=\"ansired\">]: </span>[&apos;games_features_hudi_tour_1&apos;,\n",
       " &apos;games_features_1&apos;,\n",
       " &apos;teams_features_1&apos;,\n",
       " &apos;games_features_on_demand_tour_1&apos;,\n",
       " &apos;attendances_features_1&apos;,\n",
       " &apos;players_features_1&apos;,\n",
       " &apos;season_scores_features_1&apos;]</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_featuregroups(featurestore=featurestore.project_featurestore())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List all Features in a Feature Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"><span class=\"ansired\">Out[</span><span class=\"ansired\">44</span><span class=\"ansired\">]: </span>[&apos;away_team_id&apos;,\n",
       " &apos;home_team_id&apos;,\n",
       " &apos;_hoodie_commit_seqno&apos;,\n",
       " &apos;_hoodie_commit_time&apos;,\n",
       " &apos;_hoodie_file_name&apos;,\n",
       " &apos;_hoodie_partition_path&apos;,\n",
       " &apos;_hoodie_record_key&apos;,\n",
       " &apos;score&apos;,\n",
       " &apos;away_team_id&apos;,\n",
       " &apos;home_team_id&apos;,\n",
       " &apos;score&apos;,\n",
       " &apos;team_budget&apos;,\n",
       " &apos;team_id&apos;,\n",
       " &apos;team_position&apos;,\n",
       " &apos;average_attendance&apos;,\n",
       " &apos;sum_attendance&apos;,\n",
       " &apos;team_id&apos;,\n",
       " &apos;average_player_age&apos;,\n",
       " &apos;average_player_rating&apos;,\n",
       " &apos;average_player_worth&apos;,\n",
       " &apos;sum_player_age&apos;,\n",
       " &apos;sum_player_rating&apos;,\n",
       " &apos;sum_player_worth&apos;,\n",
       " &apos;team_id&apos;,\n",
       " &apos;average_position&apos;,\n",
       " &apos;sum_position&apos;,\n",
       " &apos;team_id&apos;]</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_features_list()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default get_features_list() will use the project's feature store, but this can also be specified with the optional argument featurestore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"><span class=\"ansired\">Out[</span><span class=\"ansired\">45</span><span class=\"ansired\">]: </span>[&apos;away_team_id&apos;,\n",
       " &apos;home_team_id&apos;,\n",
       " &apos;_hoodie_commit_seqno&apos;,\n",
       " &apos;_hoodie_commit_time&apos;,\n",
       " &apos;_hoodie_file_name&apos;,\n",
       " &apos;_hoodie_partition_path&apos;,\n",
       " &apos;_hoodie_record_key&apos;,\n",
       " &apos;score&apos;,\n",
       " &apos;away_team_id&apos;,\n",
       " &apos;home_team_id&apos;,\n",
       " &apos;score&apos;,\n",
       " &apos;team_budget&apos;,\n",
       " &apos;team_id&apos;,\n",
       " &apos;team_position&apos;,\n",
       " &apos;average_attendance&apos;,\n",
       " &apos;sum_attendance&apos;,\n",
       " &apos;team_id&apos;,\n",
       " &apos;average_player_age&apos;,\n",
       " &apos;average_player_rating&apos;,\n",
       " &apos;average_player_worth&apos;,\n",
       " &apos;sum_player_age&apos;,\n",
       " &apos;sum_player_rating&apos;,\n",
       " &apos;sum_player_worth&apos;,\n",
       " &apos;team_id&apos;,\n",
       " &apos;average_position&apos;,\n",
       " &apos;sum_position&apos;,\n",
       " &apos;team_id&apos;]</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_features_list(featurestore=featurestore.project_featurestore())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List all Training Datasets in a Feature Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"><span class=\"ansired\">Out[</span><span class=\"ansired\">46</span><span class=\"ansired\">]: </span>[&apos;tour_training_dataset_test_1&apos;, &apos;team_position_prediction_1&apos;]</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_training_datasets()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default `get_training_datasets()` will use the project's feature store, but this can also be specified with the optional argument featurestore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"><span class=\"ansired\">Out[</span><span class=\"ansired\">47</span><span class=\"ansired\">]: </span>[&apos;tour_training_dataset_test_1&apos;, &apos;team_position_prediction_1&apos;]</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_training_datasets(featurestore=featurestore.project_featurestore())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### List all Storage Connectors in a Feature Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"><span class=\"ansired\">Out[</span><span class=\"ansired\">48</span><span class=\"ansired\">]: </span>[(&apos;demo_featurestore_admin000_featurestore&apos;, &apos;JDBC&apos;),\n",
       " (&apos;demo_featurestore_admin000&apos;, &apos;JDBC&apos;),\n",
       " (&apos;demo_featurestore_admin000_Training_Datasets&apos;, &apos;HOPSFS&apos;),\n",
       " (&apos;demo_featurestore_admin000_meb1_onlinefeaturestore&apos;, &apos;JDBC&apos;)]</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_storage_connectors()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default `get_storage_connectors()` will use the project's feature store, but this can also be specified with the optional argument featurestore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"><span class=\"ansired\">Out[</span><span class=\"ansired\">49</span><span class=\"ansired\">]: </span>[(&apos;demo_featurestore_admin000_featurestore&apos;, &apos;JDBC&apos;),\n",
       " (&apos;demo_featurestore_admin000&apos;, &apos;JDBC&apos;),\n",
       " (&apos;demo_featurestore_admin000_Training_Datasets&apos;, &apos;HOPSFS&apos;),\n",
       " (&apos;demo_featurestore_admin000_meb1_onlinefeaturestore&apos;, &apos;JDBC&apos;)]</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_storage_connectors(featurestore=featurestore.project_featurestore())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get All Metadata (Features, Feature groups, Training Datasets) for a Feature Store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"><span class=\"ansired\">Out[</span><span class=\"ansired\">50</span><span class=\"ansired\">]: </span>&lt;hops.featurestore_impl.dao.common.featurestore_metadata.FeaturestoreMetadata at 0x7f881f226a90&gt;</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_featurestore_metadata()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default `get_featurestore_metadata` will use the project's feature store, but this can also be specified with the optional argument featurestore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style scoped>\n",
       "  .ansiout {\n",
       "    display: block;\n",
       "    unicode-bidi: embed;\n",
       "    white-space: pre-wrap;\n",
       "    word-wrap: break-word;\n",
       "    word-break: break-all;\n",
       "    font-family: \"Source Code Pro\", \"Menlo\", monospace;;\n",
       "    font-size: 13px;\n",
       "    color: #555;\n",
       "    margin-left: 4px;\n",
       "    line-height: 19px;\n",
       "  }\n",
       "</style>\n",
       "<div class=\"ansiout\"><span class=\"ansired\">Out[</span><span class=\"ansired\">51</span><span class=\"ansired\">]: </span>&lt;hops.featurestore_impl.dao.common.featurestore_metadata.FeaturestoreMetadata at 0x7f881f226a90&gt;</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "featurestore.get_featurestore_metadata(featurestore=featurestore.project_featurestore())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Attach Metadata to a Feature Group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The feature store enables users to attach a dictionary of metadata to a feature group. It is only supported for CachedFeatureGroups."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "featurestore.add_metadata(\"teams_features\", {\"attr1\" : \"attr1 value\", \"attr2\" : \"attr2 value\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get All Metadata associated to a Feature Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "featurestore.get_metadata(\"teams_features\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the metadata by their keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "featurestore.get_metadata(\"teams_features\", [\"attr1\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Remove metadata attached to a Feature Group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "featurestore.remove_metadata(\"teams_features\", [\"attr1\"])"
   ]
  }
 ],
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